Unnati Nigam

AI Researcher

Mumbai, Maharashtra, India3 yrs 8 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in Gaussian Process modeling and uncertainty quantification.
Developed scalable inference methods for complex dynamical systems.
Strong background in statistical modeling applied to real-world problems.

Stackforce AI infers this person is a Researcher in Statistical Modeling and Probabilistic Systems.

Contact

Skills

Core Skills

Probabilistic ModelsStatistical ModelingForecasting

Other Skills

Model ValidationTime Series AnalysisBayesian inferenceUncertainty QuantificationParameter EstimationGaussian ProcessesFast likelihood evaluationScalable inferenceControlBootstrap (Framework)Stochastic ProcessesPython (Programming Language)

About

I am a PhD researcher in statistics working on probabilistic time series models for nonstationary and structured dynamical systems. My work focuses on Gaussian Process modeling, with emphasis on quasi-periodic structure, scalable inference, and uncertainty quantification. I work on problems where statistical modeling meets real systems, including forecasting, control, and robotics. My research includes developing fast likelihood evaluation methods, parameter estimation with confidence intervals, and producing reliable predictions under uncertainty.

Experience

Monash university

Teaching Assistant

Jul 2025 – Nov 2025 · 4 mos · Melbourne, Victoria, Australia

ECE 2111: Signals and Systems
ECE 2191: Probability and AI for Engineers

Indian institute of technology, bombay

Teaching Assistant

Aug 2023 – Present · 2 yrs 7 mos · Mumbai, Maharashtra, India

SI 515: Statistical Techniques in Data Mining (Aug-Nov 2023)
MA 110: Linear Algebra and Ordinary Differential Equations (Jan-May 2024)
International Summer School (June 2024): Statistical Machine Learning
SI 427: Probability I (Aug-Nov 2024)
SI 509: Time Series Analysis (Jan-May 2025)
MA 110: Linear Algebra and Ordinary Differential Equations (Jan-May 2026)

Iitb-monash research academy

PhD Research Scholar

Jul 2022 – Present · 3 yrs 8 mos · Mumbai, Maharashtra, India

Developed probabilistic time series models for nonstationary and structured dynamical systems using Gaussian Processes.
Designed quasi-periodic Gaussian Process models with fast likelihood evaluation and scalable inference.
Performed parameter estimation and uncertainty quantification using likelihood-based methods and bootstrap confidence intervals.
Built prediction frameworks that provide reliable uncertainty estimates for forecasting and control-oriented applications.
Applied models to real systems, including robotic platforms and racecar experiments, to study prediction stability and convergence.

ForecastingModel ValidationProbabilistic ModelsStatistical ModelingTime Series AnalysisBayesian inference+3

Chegg inc.

Q and A Expert

Feb 2020 – Nov 2023 · 3 yrs 9 mos

Solved and explained advanced problems in probability, statistics, and mathematical modeling for a global student audience.
Provided clear, step-by-step reasoning for complex quantitative questions under time constraints.
Developed the ability to communicate technical concepts precisely and efficiently to non-expert audiences.